Anomaly Detection in Network Traffic using Machine Learning Techniques

Abstract

In today's increasingly interconnected digital world, ensuring the security and reliability of network traffic has become a critical concern. Traditional rule-based and signature-driven approaches to network anomaly detection are often inadequate in identifying novel and evolving cyber threats. Machine learning (ML) techniques provide a powerful solution by learning patterns from historical network traffic data and detecting deviations that may indicate security threats or network malfunctions. This paper explores the application of various machine learning models, such as supervised, unsupervised, and deep learning techniques, for real-time anomaly detection in network traffic. Key challenges, including data preprocessing, feature selection, and the handling of class imbalance, are addressed. Through a comparative analysis of algorithms such as Decision Trees, Support Vector Machines (SVMs), and Neural Networks, we demonstrate the effectiveness of machine learning models in identifying both known and unknown network anomalies. The results highlight the potential of hybrid models and ensemble techniques to improve detection accuracy and reduce false positives. This study underscores the importance of leveraging advanced machine learning techniques to strengthen network security frameworks and maintain the integrity of digital communications.